RadGame: An AI-Powered Platform for Radiology Education

Mohammed Baharoon, Siavash Raissi, John S. Jun, Thibault Heintz, Mahmoud Alabbad, Ali Alburkani, Sung Eun Kim, Kent Kleinschmidt, Abdulrahman O. Alhumaydhi, Mohannad Mohammed G. Alghamdi, Jeremy Francis Palacio, Mohammed Bukhaytan, Noah Michael Prudlo, Rithvik Akula, Brady Chrisler, Benjamin Galligos, Mohammed O. Almutairi, Mazeen Mohammed Alanazi, Nasser M. Alrashdi, Joel Jihwan Hwang, Sri Sai Dinesh Jaliparthi, Luke David Nelson, Nathaniel Nguyen, Sathvik Suryadevara, Steven Kim, Mohammed F. Mohammed, Yevgeniy R. Semenov, Kun-Hsing Yu, Abdulrhman Aljouie, Hassan AlOmaish, Adam Rodman, Pranav Rajpurkar
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:898-920, 2026.

Abstract

We introduce {RadGame}, an {AI}-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. {RadGame} addresses this gap by combining gamification with large-scale public datasets and automated, {AI}-driven feedback that provides clear, structured guidance to human learners. In {RadGame} {Localize}, players draw bounding boxes around abnormalities, which are automatically compared to radiologist-drawn annotations from public datasets, and visual explanations are generated by vision-language models for user missed findings. In {RadGame} {Report}, players compose findings given a chest X-ray, patient age and indication, and receive structured {AI} feedback based on radiology report generation metrics, highlighting errors and omissions compared to a radiologist’s written ground truth report from public datasets, producing a final performance and style score. In a prospective evaluation, participants using {RadGame} demonstrated a 68% improvement in localization accuracy compared to 17% with traditional passive methods and a 31% improvement in report-writing accuracy compared to 4% with traditional methods after seeing the same cases. {RadGame} highlights the potential of {AI}-driven gamification to deliver scalable, feedback-rich radiology training and reimagines the application of medical {AI} resources in education.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-baharoon26a, title = {RadGame: An AI-Powered Platform for Radiology Education}, author = {Baharoon, Mohammed and Raissi, Siavash and Jun, John S. and Heintz, Thibault and Alabbad, Mahmoud and Alburkani, Ali and Kim, Sung Eun and Kleinschmidt, Kent and Alhumaydhi, Abdulrahman O. and Alghamdi, Mohannad Mohammed G. and Palacio, Jeremy Francis and Bukhaytan, Mohammed and Prudlo, Noah Michael and Akula, Rithvik and Chrisler, Brady and Galligos, Benjamin and Almutairi, Mohammed O. and Alanazi, Mazeen Mohammed and Alrashdi, Nasser M. and Hwang, Joel Jihwan and Jaliparthi, Sri Sai Dinesh and Nelson, Luke David and Nguyen, Nathaniel and Suryadevara, Sathvik and Kim, Steven and Mohammed, Mohammed F. and Semenov, Yevgeniy R. and Yu, Kun-Hsing and Aljouie, Abdulrhman and AlOmaish, Hassan and Rodman, Adam and Rajpurkar, Pranav}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {898--920}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/baharoon26a/baharoon26a.pdf}, url = {https://proceedings.mlr.press/v297/baharoon26a.html}, abstract = {We introduce {RadGame}, an {AI}-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. {RadGame} addresses this gap by combining gamification with large-scale public datasets and automated, {AI}-driven feedback that provides clear, structured guidance to human learners. In {RadGame} {Localize}, players draw bounding boxes around abnormalities, which are automatically compared to radiologist-drawn annotations from public datasets, and visual explanations are generated by vision-language models for user missed findings. In {RadGame} {Report}, players compose findings given a chest X-ray, patient age and indication, and receive structured {AI} feedback based on radiology report generation metrics, highlighting errors and omissions compared to a radiologist’s written ground truth report from public datasets, producing a final performance and style score. In a prospective evaluation, participants using {RadGame} demonstrated a 68% improvement in localization accuracy compared to 17% with traditional passive methods and a 31% improvement in report-writing accuracy compared to 4% with traditional methods after seeing the same cases. {RadGame} highlights the potential of {AI}-driven gamification to deliver scalable, feedback-rich radiology training and reimagines the application of medical {AI} resources in education.} }
Endnote
%0 Conference Paper %T RadGame: An AI-Powered Platform for Radiology Education %A Mohammed Baharoon %A Siavash Raissi %A John S. Jun %A Thibault Heintz %A Mahmoud Alabbad %A Ali Alburkani %A Sung Eun Kim %A Kent Kleinschmidt %A Abdulrahman O. Alhumaydhi %A Mohannad Mohammed G. Alghamdi %A Jeremy Francis Palacio %A Mohammed Bukhaytan %A Noah Michael Prudlo %A Rithvik Akula %A Brady Chrisler %A Benjamin Galligos %A Mohammed O. Almutairi %A Mazeen Mohammed Alanazi %A Nasser M. Alrashdi %A Joel Jihwan Hwang %A Sri Sai Dinesh Jaliparthi %A Luke David Nelson %A Nathaniel Nguyen %A Sathvik Suryadevara %A Steven Kim %A Mohammed F. Mohammed %A Yevgeniy R. Semenov %A Kun-Hsing Yu %A Abdulrhman Aljouie %A Hassan AlOmaish %A Adam Rodman %A Pranav Rajpurkar %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-baharoon26a %I PMLR %P 898--920 %U https://proceedings.mlr.press/v297/baharoon26a.html %V 297 %X We introduce {RadGame}, an {AI}-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. {RadGame} addresses this gap by combining gamification with large-scale public datasets and automated, {AI}-driven feedback that provides clear, structured guidance to human learners. In {RadGame} {Localize}, players draw bounding boxes around abnormalities, which are automatically compared to radiologist-drawn annotations from public datasets, and visual explanations are generated by vision-language models for user missed findings. In {RadGame} {Report}, players compose findings given a chest X-ray, patient age and indication, and receive structured {AI} feedback based on radiology report generation metrics, highlighting errors and omissions compared to a radiologist’s written ground truth report from public datasets, producing a final performance and style score. In a prospective evaluation, participants using {RadGame} demonstrated a 68% improvement in localization accuracy compared to 17% with traditional passive methods and a 31% improvement in report-writing accuracy compared to 4% with traditional methods after seeing the same cases. {RadGame} highlights the potential of {AI}-driven gamification to deliver scalable, feedback-rich radiology training and reimagines the application of medical {AI} resources in education.
APA
Baharoon, M., Raissi, S., Jun, J.S., Heintz, T., Alabbad, M., Alburkani, A., Kim, S.E., Kleinschmidt, K., Alhumaydhi, A.O., Alghamdi, M.M.G., Palacio, J.F., Bukhaytan, M., Prudlo, N.M., Akula, R., Chrisler, B., Galligos, B., Almutairi, M.O., Alanazi, M.M., Alrashdi, N.M., Hwang, J.J., Jaliparthi, S.S.D., Nelson, L.D., Nguyen, N., Suryadevara, S., Kim, S., Mohammed, M.F., Semenov, Y.R., Yu, K., Aljouie, A., AlOmaish, H., Rodman, A. & Rajpurkar, P.. (2026). RadGame: An AI-Powered Platform for Radiology Education. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:898-920 Available from https://proceedings.mlr.press/v297/baharoon26a.html.

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